From the SMX Advanced Conference in Seattle, Washington, June 22-23, 2016. SESSION: The Mad Scientists of Paid Search. PRESENTATION: Bidding Models Complex & Simple - Given by Sandeep Dey, @sdey - Groupon Inc, Staff Data Scientist. #SMX #11B

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Optimizing Bidding for different Scenarios
Bidding Models
Complex
&
Simple

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Developing a Bidding Strategy
What do you want to do ?
1. Branded vs Transactional
2. Reach vs Efficiency
3. CPA vs CPC

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Target: Maximize Volume
• Concentrate on keywords which have low CPC but
high volume
• Queries with lower transaction intent e.g. Facebook
can sustain huge traffic even at 0.01$
• Bidding becomes less important in such a scenario,
budget maintenance becomes more important
• Bid to get Top of Page

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Target : Get revenue & new customers from spend
• Set up efficiency targets & budgets
• Go for transactional keywords
• Could be lower volume
• Mine tail keywords
• Bidding is the most important part of this strategy

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Theory of Bidding, Cost, Revenue & Efficiency
Bidding Theory

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• Clicks increase as bid
increases (linear)
• Revenue increases as bid
increases (linear)
• Cost increases as bid
increases (quadratic)
• Efficiency increases as bid
increases (linear)
How does efficiency change with bids
bid

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• Maximize volume while
hitting efficiency targets
• Profit is not the metric we
are optimizing for
Aim of the game : Hit target efficiency

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• PLA
• Revenue follows a logit function
• EGR follows a crazy slope
• Broad Match Keywords
• Cost follows an exponential trend
with bid increase
• Revenue follows a log trend with bid
increase
Not all keywords or programs are created equal

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Implementing Basic Bidding into an account
The Basics

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Revenue per click
x
Efficiency target
OR
Revenue per click
ROAS target
Bid Calculation Basics for a Keyword
Measures
• Revenue last 30 days
• Clicks last 30 days
Parameters
• Efficiency target (EGR)
• cost / revenue
• ROAS target
• revenue / cost

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• Different products sell at
different times.
• Use exponential decay on
revenue and clicks.
• Revenue from N days ago has
decay ^ N weight
• Start with 0.95 decay factor
Seasonality : Use exponential decay

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• Down jackets & sweaters sell
the most from Nov to Jan
• The sales volume decreases
by half at the end of Feb
• Without a decay , the bids
will follow seasonal trends
very slowly
Seasonality : Use exponential decay
YEAR

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• Difference between bid (max
cpc) & cost (avg cpc)
• Bids have to be incremented
by a bid headroom factor to
prevent hyper efficiency
• Bid headroom = max cpc/ avg cpc
Bid headroom

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• Say you want to pay $.90/click
• Bid up at 1.10$
• Bid up by 20%
( max cpc x clicks / cost )
Bid headroom

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• Choose a time period to
collect Revenue & Clicks
(30 days)
• Keywords with clicks >
threshold (100 clicks) can be
bid with individual info
• Bid = revenue/clicks x
efficiency target x bid
headroom
High traffic keywords

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• Group multiple keywords to
get enough clicks above a
threshold
• Grouping can be based on
adgroup & campaigns
• Grouping can be also based
on category of the keywords.
Low traffic keywords : Grouping keywords

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Low traffic keywords : Grouping keywords
• E.g. group electronics into a
single category
• Group Apparels into a single
category if needed

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• Increase date range for low traffic keywords
• Less frequent keywords : longer time ranges
• Beware too long time ranges can’t follow seasonality
Low traffic keywords : Increase date ranges

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Issues to keep in mind
while implementing a bidding strategy
The Troublemakers

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• Keep a lookout for bad
search queries & keywords
• If they have lower bids, they
will reduce bids for the group
they are clustered in
• Solution : Negative keyword
them or be aggressive in
their bid reduction
Keywords which don’t convert

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• Addition of bid modifiers
would upset your bidding
• A negative bid modifier tends
to make you hyper-efficient
• Final CPC are not equal to
what is calculated
• Solution: measure the overall
effect of bid modifiers on
CPC and adjust
Bid Modifiers

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• Remarketing lists for search
ads (RLSA)
• +100% increase for previous
visitors
• Bid is set at 1$ , but CPC >1$
• Solution : Figure out how
audiences affect the CPC in
total
Bid Modifiers

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Machine Learned Models for Bidding for an ecommerce
Advanced Bidding

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• Transactional Text Ads
Bidding
• Campaign Strategy centered
on either landing on Product
Page or Search Page
• Modeled into a Machine
Learning Problem with
targets as revenue per click
Advanced Bidding for Ecommerce

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Target feature :
Future Revenue Per Click
Input Features :
• Historical Revenue Per Click
• Margin & Price
• Discounts
• Matchtype
• Keyword Relevance to Product Title
Performance :
• Revenue lift of +20% with cost increase of +10%
as compared to baseline
Predictive Modeling at a keyword level

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LEARN MORE: UPCOMING @SMX EVENTS
THANK YOU!
SEE YOU AT THE NEXT #SMX